Physiological parameter monitoring system

ABSTRACT

Data streams are received from each of the plurality of sensors. These data streams comprise varying values generated by the sensors and characterize an associated physiological parameter. A parameter score is repeatedly determined for each physiological sensor that is based on whether the varying values for the associated physiological parameter deviate from at least one pre-defined threshold. A patient health index is repeatedly generated by combining each of the determined parameter scores to characterize an overall health of the patient. Data characterizing the patient health index is repeatedly provided. Related apparatus, systems, techniques and articles are also described.

TECHNICAL FIELD

The subject matter described herein relates to physiological parametermonitoring systems and related methods, including graphical userinterfaces, for characterizing health of a patient as measured by aplurality of physiological sensors coupled to a patient monitor.

BACKGROUND

The assessment of a patient's current health status by a health careprofessional is typically performed by observation of the patient'svital signs, such as blood pressure, heart rate, respiratory rate, bloodoxygenation and the like as measured by various physiological sensorsand as displayed by a patient monitor. These physiological parametersare measured and displayed separately, but compared against each otherand evaluated in their entirety by the health care professional based onhis or her clinical experience.

SUMMARY

In one aspect, a system includes a communications interface, at leastone programmable data processor, and memory. The communicationsinterface is configured to receive data streams from a plurality ofphysiological sensors with each sensor measuring a differentphysiological parameter of a patient. The memory can store instructionswhich, when executed by the at least one programmable data processor,result in various operations. These operations can include receiving oneor more data streams from each of the plurality of sensors via thecommunications interface. The one or more data streams can includevarying values generated by the sensor and characterizing the associatedphysiological parameter. The operations can also include repeatedlydetermining, for each physiological sensor, a parameter score based onwhether the varying values for the associated physiological parameterdeviate from at least one pre-defined threshold. The operations can alsoinclude repeatedly generating a patient health index by combining eachof the determined parameter scores to characterize an overall health ofthe patient. Further, the operations can include repeatedly providingdata characterizing the patient health index.

The repeatedly determining, repeatedly generating and repeatedlyproviding can be performed on a periodic basis.

The repeatedly determining, repeatedly generating and repeatedlyproviding can, in some variations, be performed on a continuous basis.

The providing data can include one or more of: displaying the datacharacterizing the patient health index in an electronic display device,loading the data characterizing the patient health index into thememory, storing the data characterizing the patient health index inpersistent memory, transmitting the data characterizing the patienthealth index to a remote computing system, or generating an audio,vibrational and/or visual alert characterizing the patient health index.

A magnitude of the deviations from the at least one pre-definedthreshold can be repeatedly calculated, and factors can be repeatedlyallocated to the magnitude of the deviations which are used to generatethe determined parameter scores. The allocated factors can betime-averaged over a pre-defined time window and they can be used togenerate the determined parameter scores.

The generation of the patient health index can assign weights to each ofthe determined parameter scores as part of the combining. The weightscan vary depending on an amount of time and/or a severity of deviationfrom the at least one pre-defined threshold.

Segments of time can be identified during which the values in the datastreams are unreliable. In response, the associated parameter score forthe corresponding physiological sensor can be adjusted to exclude suchidentified segments. The adjusting can include using values frompreceding or successive segments of time relative to the identifiedsegments of time for the identified segment of time when generating theassociated parameter score. In addition or the alternative, theadjusting can include using factors from preceding or successivesegments of time relative to the identified segments of time for theidentified segment of time when generating the associated parameterscore.

In some implementations, the providing of data can include displaying,in a graphical user interface, a visualization displaying the repeatedlygenerated patient health index over time in relation to the repeatedlydetermined parameter scores. Such a visualization can further displaysparameter scores used to generate the repeatedly determined patienthealth index. A color of at least a portion of the visualization canvary depending on the allocated factors. The visualization can furtherdisplay at least one of the pre-defined thresholds. Further, a color ofat least a portion of the visualization can varies depending ondeviations from the pre-defined thresholds.

In an interrelated aspect, data streams are received from each of theplurality of sensors. These data streams comprise varying valuesgenerated by the sensors and characterize an associated physiologicalparameter. A parameter score is repeatedly determined for eachphysiological sensor that is based on whether the varying values for theassociated physiological parameter deviate from at least one pre-definedthreshold. A patient health index is repeatedly generated by combiningeach of the determined parameter scores to characterize an overallhealth of the patient. Data characterizing the patient health index isrepeatedly provided.

Non-transitory computer program products (i.e., physically embodiedcomputer program products) are also described that store instructions,which when executed by one or more data processors of one or morecomputing systems, cause at least one data processor to performoperations herein. Similarly, computer systems are also described thatcan include one or more data processors and memory coupled to the one ormore data processors. The memory can temporarily or permanently storeinstructions that cause at least one processor to perform one or more ofthe operations described herein. In addition, methods can be implementedby one or more data processors either within a single computing systemor distributed among two or more computing systems. Such computingsystems can be connected and can exchange data and/or commands or otherinstructions or the like via one or more connections, including but notlimited to a connection over a network (e.g., the Internet, a wirelesswide area network, a local area network, a wide area network, a wirednetwork, or the like), via a direct connection between one or more ofthe multiple computing systems, etc.

The subject matter described herein provides many technical advantages.For example, the current subject matter provides techniques forcharacterizing and visualizing a current wellbeing of a patient thattakes into account dynamically changing individual patientcharacteristics, development over time, and knowledge of the history anddiagnosis of the patient.

The details of one or more variations of the subject matter describedherein are set forth in the accompanying drawings and the descriptionbelow. Other features and advantages of the subject matter describedherein will be apparent from the description and drawings, and from theclaims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram illustrating a patient monitor coupled toa patient;

FIG. 2 is a process flow diagram illustrating calculation of aphysiological parameter score;

FIG. 3 is a diagram illustrating a methodology of allocation of factorsto values from data streams generated by one or more physiologicalsensors;

FIG. 4 is a diagram illustrating allocation of a final physiologicalparameter score based on allocated factors of values from data streamsgenerated by one or more physiological sensors;

FIG. 5 is a diagram illustrating generation of a patient health indexfrom a plurality of physiological parameter scores;

FIG. 6 is a diagram illustrating individual physiological parameterswith associated parameter scores and an overall patient health index;

FIG. 7 is a diagram illustrating how weights can be assigned by thepatient monitor through automated analysis of physiological parametertrends;

FIG. 8 is a diagram illustrating exclusion of a noisy segment fromfactor and patient health index computation;

FIG. 9 is a graphical user interface view illustrating physiologicalparameter scores, patient health index values, and relative individualphysiological parameter score contributions to the patient health index;

FIG. 10 is a diagram illustrating the calculation of relative individualparameter contributions to a patient health index for display on agraphical user interface;

FIG. 11 is a process flow diagram illustrating computation andvisualization of a patient health index; and

FIG. 12 is a diagram illustrating a computing device for implementingaspects of the current subject matter.

DETAILED DESCRIPTION

The current subject matter is directed to a physiological monitoringsystem that characterizes the health of a patient based on multiple,dynamically changing physiological parameters as measured by variousphysiological sensors connected to the patient and to a patient monitor.

FIG. 1 is a diagram 100 illustrating an example implementation in whichphysiological parameters of a patient, including, for example, bloodpressure, heart rate, respiratory rate, and blood oxygenation of apatient 110 are measured by a patient monitor 130. The patient monitor130 can include memory 180 for storing instructions for execution by oneor more processor/processor cores 150. Memory 180 can also be capable ofstoring parameter data and related visualizations. The patient monitor130 can include a display 160 for rendering visual information thatcorresponds to the physiological parameters (e.g., values, waveforms,etc.). In addition, the patient monitor 130 can also include aninterface 140 that permits for wired or wireless communication with oneor more physiological sensors 120 and/or a remote medical device and/ora remote computing system or network to transmit/receive physiologicalparameter data (e.g., vital signs, etc.). One example, of aphysiological sensor 120 is an electrocardiogram (ECG) electrode setthat includes, for example, a right arm electrode, a left arm electrode,and a left leg electrode. Other types of physiological sensors 120include, for example, invasive blood pressure transducers, bloodoxygenation (e.g., SpO2 finger cuff, etc.), and respiration sensors(i.e., to detect, for example, apnea and other breathing abnormalities,etc.). The physiological sensors 120 can each transmit/generate one ormore data streams to the communications interface 140. These datastreams can include varying values generated by the physiological sensor120 that characterize the associated physiological parameter (e.g.,varying vital signs for the patient, etc.).

Patient monitor 130 can transmit data characterizing the physiologicalparameters of the patient 110 to a remote computing system (e.g.,medical device, back-end computing system, etc.) via the communicationsinterface 140. Patient monitor 130 can also include an audible alarmthat can sound from an audio output 170 alerting a patient and/ormedical staff. Alarms can also be conveyed by via the display 160 orother visual alert mechanisms (not shown).

The patient monitor 130 can, using the data streams received by theinterface from the physiological sensors 120 and the processor(s) 150,implement, an algorithm for calculating a patient health index (PHI).The algorithm can weigh deviations of the varying values for theassociated physiological parameter from at least one pre-definedthreshold (sometimes referred to herein as limit violations) dependingon the severity/magnitude of the respective deviation (e.g., warning,serious, safety, etc.). The average of the weights over a predefinedtime period can result in a parameter score, and a combination of thesescores for different physiological parameters can results in the patienthealth index (which dynamically changes). The patient health index is ameasure for the current health status of the patient which also gives anindication about status development of the patient towards healthimprovement or deterioration. This information allows for betterjudgment of the current status and the future heath development of thepatient.

FIG. 2 is a process flow diagram 200 illustrating implementation of analgorithm for calculating a patient health index. Initially a score foreach individual parameter is computed based on the individual upper andlower alarm limit values considered appropriate for the respectivepatient based on his/her diagnosis and assessment when admitted to thehospital. More than one upper and lower alarm limit can be definedrepresenting increasing parameter deviation from normal physiologicallevels and therefore increasing health risk. The algorithm weights everylimit violation depending on the severity of the respective violation(in this example three limits are used, denominated as warning, serious,and safety limits; warning representing a low, serious a medium, andsafety a high patient health risk limit). For example, an initial statusvalue of 100% (trend within normal physiological range) is reduced by afactor f1, f2 or f3 based on the magnitude of the deviation from thenormal range. For example, if the trend comes to lie between warning andserious limits, the initial value is multiplied by factor f1 (say by0.7, reducing the status value to 70%), if the parameter value furtherworsens and comes to lie between serious and safety limits it ismultiplied by another factor f2 indicative of the worsening (say by 0.3,reducing the status value to 30%), if it crosses the safety thresholdthe status value drops to 0%.

With continued reference to diagram 200 of FIG. 2 , initially limits 204are defined (for example, using default values and/or by the healthcareprofessional inputting values into a graphical user interface, or by(semi-)automatic determination through an algorithm). Trend information208 which is or is derived from the data streams outputted by thephysiological sensors 120 is analyzed to determine whether the valueswithin such data streams exceed respective thresholds 212, 216; 224,228; and 236, 240 which respectively correspond to categories of factors232, 244, 248 (which in this example correspond to warning factor,serious factor, and safety factor). If none of the thresholds areexceeded, then a normal factor 220 can be assigned at any given moment.Further, a window 252 can be defined/established which specifies a timeinterval before t for computation of the physiological parameter score.The physiological parameter score 256 can then be calculated as anaverage of the factors over the window. The physiological parameterscore 256 can be continuously calculated or alternatively on a periodicor on-demand basis.

The result of the factor allocation of FIG. 2 is shown in diagram 300 ofFIG. 3 for an arbitrary physiological parameter trend. Diagram 300 can,for example, be a view rendered in a graphical user interface displayedon the display 160 of patient monitor 130. The allocated factors pertime point are shown in the graph at the bottom. The trend starts with awarning limit violation and is therefore allocated factor f1 (in thisexample 0.7). When the trend goes back below the warning limit thestatus value in the lower graph increases to 1 (i.e. 100%, normalfactor). Subsequent limit violations of the serious limit and safetylimit are consequently allocated the respective factors f2 and f3 forthe duration of the violation.

The final physiological parameter score for one time point can beobtained by averaging the factors over a time window preceding that timepoint as illustrated in diagram 400 of FIG. 4 (which can also berendered in a graphical user interface displayed on display 160 of thepatient monitor 130). This diagram 400 illustrates a measure of therelative time that a parameter is within normal physiological rangesduring the past time window. Averaging can help dampen strong influenceof short term changes or artifacts on the overall status assessment.Time windows can be of different length such as 30, 60, 90 minutes, andthe like. The resulting score graph allows an interpretation of thecurrent physiological parameter status in which its slope gives anindication about the development of the parameter compared to previoustimes points. A negative slope indicates that new time points added tothe averaging time window contribute negatively to the patient score,i.e. increasing parts of the observed trend violate some limit comparedto previous time points, and the overall score is therefore decreasing.Conversely, a positive slope indicates that new time points contributingto the score add trend values that are within normal physiologicallimits, or at least deviate less from the warning limit.

The scores of each parameter (i.e. the averaged factor values) can thenbe combined for each time point into an overall patient health index(PHI). Such a combination can be done in a variety of manners includingmultiplication as illustrated in diagram 500 of FIG. 5 . Diagram 600 ofFIG. 6 illustrates how the individual physiological parameters withassociated parameter scores can be combined to form the PHI. Forexample, if the parameters heart rate, SpO2 and mean arterial pressureare measured, and they have respective individual scores of 70%, 70%,and 100%, then the combined score would be (0.7*0.7*1)*100%=49%. Thecombination reflects and reproduces the integrated evaluation ofmultiple parameters by the healthcare professional. Small deviationsfrom normal values for one single parameter may not represent danger forthe patient, however small simultaneous deviations of several parametersmay indicate some relevant physiological changes for which thehealthcare professional should be alerted. By multiplication, smalldeviations of several parameters amplify each other and result in alarger decrease of the PHI.

Using the algorithm provided herein, all available physiologicalmeasurements can be combined by the patient monitor 130 to assesspatient status without requirement of specific parameters. Thehealthcare professional can choose to include all measured parameters ordiscard those that are irrelevant or might falsify the patient healthindex, depending on individual diagnosis and medication. The statuscomputation depends on the individually selected/computed alarm limitsfor each patient and therefore adapts to the specific condition.Averaging allows for evaluation of patient status over a time period andtherefore for the estimation of the status development towardsimprovement or decline of patient health.

In addition to the patient health index computation described above, thepatient monitor 130 can implement variations based on the individualpreferences in different intensive care units, patient assessment,diagnosis and the like. One such variation can take into account therelevance of different physiological parameters (as measured by thephysiological sensors 120) for different diagnoses. Some healthconditions may require one physiological parameter to have higherrelevance than others. Such a physiological parameter can be assigned aweight in the multiplication process to better reflect its criticalityto the patient condition.

In addition to user-specified weights, weights can be assigned by thepatient monitor 130 through automated analysis of the parameter trend. Aparameter value that is very close to the upper/lower limit for a longtime before actually crossing it is more likely to represent a truephysiological condition than a parameter value suddenlyincreasing/decreasing from the center of the value range considered asphysiologically normal (sudden change could be due to artifact). Thetime spent within a margin region below/above the upper/lower limit ishence analyzed and used for assignment of weights. An example of suchanalysis is shown in diagram 700 of FIG. 7 .

With the example of FIG. 7 , the number of samples that are close to theupper/lower limit within the considered time window are counted andcompared to the total sample number in the window, resulting in apercentage value that is used for weight allocation. The upper rightwindow shows the trend in which four time windows are highlighted andnumbered. Histograms counting the number of samples per amplitude withinthe windows are shown on the left labeled with the respective number ofthe corresponding time window. Depending on the percentage of the samplepoints that lie within the margin region) before a limit violationoccurs, the weight can be adjusted and multiplied to the factor for thecurrent limit violation. Within time window 1, for instance, only 25% ofthe trend lies in the margin region. If the trend would cross thewarning limit right after the end of time window 1, a weight of 1 wouldbe multiplied to the factor to reflect the mainly non-critical previousparameter trend. The same is valid for time window 3. Approximately halfof the parameter values within this window are outside the marginregion, so the limit violation right afterwards is only assigned itsregular factor. However, looking at time window 4, all of the parametervalues lie within the margin and this does not change until the nextlimit violation occurs. The next limit violation therefore is weightedby multiplying a weight <1 to the factor and hence reducing the finalparameter score. Long term limit violations can in a similar way lead toan adjustment of the weighting of the respective parameter factors.

In addition to limit violations, the algorithm can take into accountother clinically relevant conditions, like arrhythmias and otherabnormal heart beats, when computing the patient health index. Thefactor assignment for the time points affected by such conditions canreflect the urgency and relevance of the condition, in which, forexample, events like ventricular fibrillation (high priory, veryserious) is directly assigned a factor of zero while less criticalevents like bigeminy can be factored in with a different valuereflecting lower immediate risk. The respective parameter factor valuegets overwritten, in this example the heart rate. The computation of theaverage parameter score and the overall status (PHI) remain the same.

Cardiac conditions that are assessed over longer time periods, like PVC(minutes), HRV (hours) or atrial fibrillation (hours) burden can, insome implementations, not be averaged (because they are inherently longterm averages) and can directly be allocated a factor based on therespective burden limits.

If signal quality indices (SQIs) are available, they should beintegrated into the computation of the PHI. SQIs can be obtained viavarious mechanisms, including, for example, setting and testing oflogical constraints on the physiological values measured by thephysiological sensors 120, feature extraction and analysis, or frequencydomain analysis. SQIs can be calculated on a single parameter basis toassess the quality of the signal and can represent the confidence in thecalculated parameter values. SQI are often represented as a percentagewhere 0% means the signal is very noisy and should not be used and 100%is a very clean signal where we expect all calculated parameter valuesto be accurate. SQIs can be signal to noise ratio calculations, or inthe case of ECG, an assessment of the stability of QRS complexes overtime (see, for example, U.S. Pat. No. 9,042,973, the contents of whichare hereby fully incorporated by reference). SQIs can also be determinedin various manners including by using a neural network or other machinelearning algorithm that, in turn, uses various features of the signal todetermine its quality.

The artifact burden during the chosen computation time window isassessed. The integration of SQIs can depend on the amount of artifactsdetected during the time window, i.e. the percentage of time that therespective parameter is subjected to artifacts rather than clean signal.This results in two possible integration arrangement. First, for a timewindow of e.g. 60 minutes, artifacts that in total result in a fewseconds or minutes of noisy signal will not have a strong influence onthe final value of the PHI as calculated by the patient monitor 130. Thenoisy segments can hence be assumed to have the same characteristics aspreceding and/or successive time points and the PHI can be computed byreplacing the factors of the noisy segments with factor valuesbefore/after the noisy segment. Second, in case the parameter exposeslong stretches of low SQI, it can be deemed unreliable to reflect realphysiological conditions and should not be used for PHI computation bythe patient monitor 130. A respective message has to be displayed to theuser detailing the parameters included in and excluded from thecomputation.

An example for the exclusion of a noisy segment from the factor andscore computation is shown in diagram 800 of FIG. 8 . The upper windowshows an arterial waveform with the dominant noisy segment highlighted,and the mean arterial pressure trend overlaid, as well as all upper andlower limit levels. The score computed ignoring the SQI information isshown in the middle window and the lowest window shows the score if theperiod of low SQI is excluded.

The graphical user interface displayed in the display 160 of the patientmonitor, can render a three level visualization method as illustrated indiagram 900 of FIG. 9 . At the highest level, the overall patient scorecan be given as a percentage number next to a coded bar (e.g., colorcoded bar, etc.) and an estimation of the current health developmenttrend (decreasing/stable/increasing). Clicking on a graphical userinterface corresponding to the bar can open a multi-parameter score(i.e. PHI) view over time that can be zoomed in and out (this viewreplaces the bar). Clicking on a graphical user interface element in thegraphical user interface corresponding to the graph can further openindividual parameter scores that result in the overall PHI so that theindividual contributions can be assessed (this view replaces the overallPHI view). Clicking on a graphical user interface element correspondingto this view can bring back the initial bar.

The PHI visualization (middle level) can be further extended, forexample, through, for example, check box graphical user interfaceelement selection. This advanced display mode can allow for selection ofdifferent curves and values to be displayed within the same graph in thegraphical user interface. Selection options can include, for example,displaying the PHI and all physiological parameter scores in the samegraph or displaying the physiological parameter score values at a giventime point. The time point can be selected by sliding the mouse (orfinger for a touch screen, etc.) to the respective position of the PHIcurve. The score values for the selected time point can be displayed inthe graphical user interface in a box that automatically adapts itsposition so that it ideally does not cover the PHI curve. The values inthe box can be instantaneously updated to the new mouse/finger position,i.e. they change with every small movement. Another option can be todisplay the relative physiological parameter contribution to the PHI.This arrangement is an intuitive way of conveying to the user theinfluence of each individual parameter on the current PHI (withoutoverlaying all parameter curves in one graph, which might easily becomeconfusing).

The computation of the relative contributions is further elucidated indiagram 1000 of FIG. 10 . For each parameter p_(n) at time point t, itsrelative contribution c_(n)(t) can be derived by scaling the amount ofdeviation of the PHI from 100% by the individual parameter score dividedby the sum of all N individual score deviations from 100%. In otherwords, the value of c_(n)(t), i.e. the thickness of the respective layerin the graph, can represent the relative influence that parameter n hashad in the production of the PHI at the respective time point. Forexample, the PHI decrease at t=92 is only due to p₁ and p₃, since p₂ isperfect (100%). The parameter being mainly responsible for the PHI atthis time point is p₃ as can be seen from the comparatively large c₃value and small c₁ value.

While the above is described in reference to a patient monitor 130 whichcan, for example, be positioned next to the bedside of the patient 110,it will be appreciated that the PHI can be implemented by remotecomputing systems provided that the data streams from the physiologicalsensors 120 are made available (directly or over a computing network).Further, the algorithms provided herein can be used for retrospectiveanalysis of the patient. For example, regions of low overall PHIdirectly point towards interesting periods for further investigation,without having to look at each individual alarm recorded. Thephysiological data can, in some cases, be automatically annotated forsubsequent review because low patient health indices indicate clinicallyrelevant events.

FIG. 11 is a process flow diagram 1100 in which, at 1110, data streamsare received from each of a plurality of sensors that include varyingvalues generated by the sensor and which characterize an associatedphysiological parameter. A parameter score is then repeatedlydetermined, at 1120, for each physiological sensor that is based onwhether the varying values for the associated physiological parameterdeviate from at least one pre-defined threshold. A patient health indexis repeatedly generated, at 1130, by combining each of the determinedparameter scores to characterize an overall health of the patient. Datais then repeatedly provided, at 1140, that characterizes the patienthealth index score. Provided, in this regard, can include one or moreof: displaying the data characterizing the patient health index score inan electronic display device, loading the data characterizing thepatient health index score into the memory, storing the datacharacterizing the patient health index score in persistent memory,transmitting the data characterizing the patient health index score to aremote computing system, or generating an audio, vibrational and/orvisual alert characterizing the patient health index score.

One or more aspects or features of the subject matter described hereincan be realized in digital electronic circuitry, integrated circuitry,specially designed application specific integrated circuits (ASICs),field programmable gate arrays (FPGAs) computer hardware, firmware,software, and/or combinations thereof. These various aspects or featurescan include implementation in one or more computer programs that areexecutable and/or interpretable on a programmable system including atleast one programmable processor, which can be special or generalpurpose, coupled to receive data and instructions from, and to transmitdata and instructions to, a storage system, at least one input device,and at least one output device. The programmable system or computingsystem can include clients and servers. A client and server aregenerally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

These computer programs, which can also be referred to as programs,software, software applications, applications, components, or code, caninclude machine instructions for a programmable processor, and/or can beimplemented in a high-level procedural language, an object-orientedprogramming language, a functional programming language, a logicalprogramming language, and/or in assembly/machine language. As usedherein, the term “computer-readable medium” refers to any computerprogram product, apparatus and/or device, such as for example magneticdiscs, optical disks, solid-state storage devices, memory, andProgrammable Logic Devices (PLDs), used to provide machine instructionsand/or data to a programmable data processor, including amachine-readable medium that receives machine instructions as acomputer-readable signal. The term “computer-readable signal” refers toany signal used to provide machine instructions and/or data to aprogrammable data processor. The computer-readable medium can store suchmachine instructions non-transitorily, such as for example as would anon-transient solid-state memory or a magnetic hard drive or anyequivalent storage medium. The computer-readable medium canalternatively or additionally store such machine instructions in atransient manner, such as for example as would a processor cache orother random access memory associated with one or more physicalprocessor cores.

The computer components, software modules, functions, data stores anddata structures described herein can be connected directly or indirectlyto each other in order to allow the flow of data needed for theiroperations. It is also noted that a module or processor includes but isnot limited to a unit of code that performs a software operation, andcan be implemented for example as a subroutine unit of code, or as asoftware function unit of code, or as an object (as in anobject-oriented paradigm), or as an applet, or in a computer scriptlanguage, or as another type of computer code. The software componentsand/or functionality can be located on a single computer or distributedacross multiple computers depending upon the situation at hand.

FIG. 12 is a diagram 1200 illustrating a sample computing devicearchitecture for implementing various aspects described herein. A bus1204 can serve as the information highway interconnecting the otherillustrated components of the hardware. A processing system 1208 labeledCPU (central processing unit) (e.g., one or more computerprocessors/data processors at a given computer or at multiplecomputers), can perform calculations and logic operations required toexecute a program. A non-transitory processor-readable storage medium,such as read only memory (ROM) 1212 and random access memory (RAM) 1216,can be in communication with the processing system 1208 and can includeone or more programming instructions for the operations specified here.Optionally, program instructions can be stored on a non-transitorycomputer-readable storage medium such as a magnetic disk, optical disk,recordable memory device, flash memory, or other physical storagemedium.

In one example, a disk controller 1248 can interface one or moreoptional disk drives to the system bus 1204. These disk drives can beexternal or internal floppy disk drives such as 1260, external orinternal CD-ROM, CD-R, CD-RW or DVD, or solid state drives such as 1252,or external or internal hard drives 1256. As indicated previously, thesevarious disk drives 1252, 1256, 1260 and disk controllers are optionaldevices. The system bus 1204 can also include at least one communicationport 1220 to allow for communication with external devices (e.g.,physiological sensors 120, etc.) either physically connected to thecomputing system or available externally through a wired or wirelessnetwork. In some cases, the communication port 1220 includes orotherwise comprises a network interface.

To provide for interaction with a user, the subject matter describedherein can be implemented on a computing device having a display device1240 (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display)monitor) for displaying information obtained from the bus 1204 to theuser and an input device 1232 such as keyboard and/or a pointing device(e.g., a mouse or a trackball) and/or a touchscreen by which the usercan provide input to the computer. Other kinds of input devices 1232 canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback (e.g.,visual feedback, auditory feedback by way of a microphone 1236, ortactile feedback); and input from the user can be received in any form,including acoustic, speech, or tactile input. In the input device 1232and the microphone 1236 can be coupled to and convey information via thebus 1204 by way of an input device interface 1228. Other computingdevices, such as dedicated servers, can omit one or more of the display1240 and display interface 1224, the input device 1232, the microphone1236, and input device interface 1228.

In the descriptions above and in the claims, phrases such as “at leastone of” or “one or more of” can occur followed by a conjunctive list ofelements or features. The term “and/or” can also occur in a list of twoor more elements or features. Unless otherwise implicitly or explicitlycontradicted by the context in which it is used, such a phrase isintended to mean any of the listed elements or features individually orany of the recited elements or features in combination with any of theother recited elements or features. For example, the phrases “at leastone of A and B;” “one or more of A and B;” and “A and/or B” are eachintended to mean “A alone, B alone, or A and B together.” A similarinterpretation is also intended for lists including three or more items.For example, the phrases “at least one of A, B, and C;” “one or more ofA, B, and C;” and “A, B, and/or C” are each intended to mean “A alone, Balone, C alone, A and B together, A and C together, B and C together, orA and B and C together.” In addition, use of the term “based on,” aboveand in the claims is intended to mean, “based at least in part on,” suchthat an unrecited feature or element is also permissible.

The subject matter described herein can be embodied in systems,apparatus, methods, and/or articles depending on the desiredconfiguration. The implementations set forth in the foregoingdescription do not represent all implementations consistent with thesubject matter described herein. Instead, they are merely some examplesconsistent with aspects related to the described subject matter.Although a few variations have been described in detail above, othermodifications or additions are possible. In particular, further featuresand/or variations can be provided in addition to those set forth herein.For example, the implementations described above can be directed tovarious combinations and subcombinations of the disclosed featuresand/or combinations and subcombinations of several further featuresdisclosed above. In addition, the logic flows depicted in theaccompanying figures and/or described herein do not necessarily requirethe particular order shown, or sequential order, to achieve desirableresults. Other implementations may be within the scope of the followingclaims.

1-33. (canceled)
 34. A system comprising: a communications interface toreceive data streams from a plurality of physiological sensors, each ofthe plurality of sensors measuring a different physiological parameterof a patient; at least one programmable data processor; and memorystoring instructions which, when executed by the at least oneprogrammable data processor, result in operations comprising: receivingone or more data streams from each of the plurality of physiologicalsensors via the communications interface, the one or more data streamscomprising varying values generated by the sensor and characterizing theassociated physiological parameter; determining, at least once everysecond, and for each of the plurality of physiological sensors, aparameter score; wherein determining the parameter score comprises, foreach of the plurality of physiological sensors: calculating a magnitudeof a plurality of varying values generated by the physiological sensor;determining which of a plurality of predetermined ranges the calculatedmagnitude falls into; allocating a predetermined factor to thecalculated magnitude according to which of the plurality ofpredetermined ranges the calculated magnitude falls into, the allocatedfactor being added to a time-average of allocated factors over a timewindow and used to generate the parameter score determined for thesensor, wherein the plurality of predetermined factors are the same foreach of the plurality of physiological sensors; generating, at leastonce every second, a patient health index, by combining the parameterscores determined for each of the plurality of sensors to characterizean overall health of the patient; and providing, at least once everysecond, data characterizing the patient health index.
 35. The system ofclaim 34, wherein the plurality of predetermined ranges comprises threepredetermined ranges.
 36. The system of claim 34, wherein the operationsfurther comprise: time-averaging the allocated factors over apre-defined time window, the time-averaged allocated factors being usedto generate the parameter score determined for each of the plurality ofphysiological sensors.
 37. The system of claim 36, wherein determiningthe parameter score comprises, for each of the plurality ofphysiological sensors, assigning a weight to one or more of thephysiological parameters sensors when combining the parameter scoredetermined for each of the plurality of physiological sensors.
 38. Thesystem of claim 37, wherein the weight varies depending on an amount oftime and/or a severity of the calculated deviation from the at least onepre-defined threshold.
 39. The system of claim 34, wherein the pluralityof physiological parameters is selected by a clinician from among aplurality of available physiological parameters
 40. The system of claim36, wherein the data characterizing the patient health index is computedaccording to the formula${{PHI}(t)} = {\prod\limits_{i = 1}^{N}{{score}\left( {p_{i},t} \right)}}$where PHI(t) is the patient health index at time t, and score(p_(i), t)is the parameter score for physiological parameter i, where 1<i<N. 41.The system of claim 34, wherein the providing the data comprises atleast one of: displaying the data characterizing the patient healthindex in an electronic display device; loading the data characterizingthe patient health index into the memory; storing the datacharacterizing the patient health index in persistent memory;transmitting the data characterizing the patient health index to aremote computing system; and generating an audio, vibrational and/orvisual alert characterizing the patient health index.
 42. The system ofclaim 34, wherein the operations further comprise: identifying segmentsof time during which the values in the data streams are unreliable; andadjusting the parameter score determined for each of the plurality ofphysiological sensors to exclude the identified segments of time duringwhich the values in the data stream are unreliable.
 43. The system ofclaim 41, wherein the adjusting comprises: using values from precedingor successive segments of time relative to the identified segments oftime when determining the parameter score for each of the plurality ofphysiological sensors.
 44. The system of claim 41, wherein the adjustingcomprises: using factors from preceding or successive segments of timerelative to the identified segments of time for the identified segmentof time when determining the parameter score for each of the pluralityof physiological sensors.
 45. A method for a system including acommunications interface configured to receive data streams from aplurality of physiological sensors, each of the plurality ofphysiological sensors measuring a different physiological parameter of apatient, the method comprising: receiving, from each of the plurality ofphysiological sensors, data streams comprising varying values generatedby the sensor and characterizing an associated physiological parameter;determining, at least once every second, and for each of the pluralityof physiological sensors, a parameter score wherein determining theparameter score comprises, for each of the plurality of physiologicalsensors; calculating a magnitude of the varying values generated by thephysiological sensor; determining which of a plurality of predeterminedranges the calculated magnitude falls into; allocating a firstpredetermined factor to the calculated magnitude when the calculatedmagnitude falls into a first of the three predetermined ranges,allocating a second predetermined factor to the calculated magnitudewhen the calculated magnitude falls into a second of the threepredetermined ranges, and allocating a third predetermined factor to thecalculated magnitude when the calculated magnitude falls into a third ofthe three predetermined ranges allocated factor being added to atime-average of allocated factors over a time window and used togenerate the parameter score determined for the sensor, wherein thefirst, second, and third predetermined factors are the same for each ofthe plurality of physiological sensors; generating, at least once everysecond, a patient health index by combining the parameter scoresdetermined for each of the plurality of sensors to characterize anoverall health of the patient; and providing data characterizing thepatient health index at least once every second.
 46. The method of claim45, wherein the plurality of predetermined ranges comprises threepredetermined ranges.
 47. The method of claim 45, wherein the operationsfurther comprise: time-averaging the allocated factors over apre-defined time window, the time-averaged allocated factors being usedto generate the parameter score determined for each of the plurality ofphysiological sensors.
 48. The method of claim 47, wherein determiningthe parameter score comprises, for each of the plurality ofphysiological sensors, assigning a weight to one or more of thephysiological parameters sensors when combining the parameter scoredetermined for each of the plurality of physiological sensors.
 49. Themethod of claim 48, wherein the weight varies depending on an amount oftime and/or a severity of the calculated deviation from the at least onepre-defined threshold.
 50. The method of claim 45, wherein the pluralityof physiological parameters is selected by a clinician from among aselection of available physiological parameters
 51. The method of claim48, wherein the data characterizing the patient health index is computedaccording to the formula${{PHI}(t)} = {\prod\limits_{i = 1}^{N}{{score}\left( {p_{i},t} \right)}}$where PHI(t) is the patient health index at time t, and score(p_(i), t)is the parameter score for physiological parameter i, where 1<i<N. 52.The method of claim 45, wherein the providing the data comprises atleast one of: displaying the data characterizing the patient healthindex in an electronic display device; loading the data characterizingthe patient health index into the memory; storing the datacharacterizing the patient health index in persistent memory;transmitting the data characterizing the patient health index to aremote computing system; and generating an audio, vibrational and/orvisual alert characterizing the patient health index.
 53. The method ofclaim 45, wherein the operations further comprise: identifying segmentsof time during which the values in the data streams are unreliable; andadjusting the parameter score determined for each of the plurality ofphysiological sensors to exclude the identified segments of time duringwhich the values in the data stream are unreliable.
 54. The method ofclaim 53, wherein the adjusting comprises: using values from precedingor successive segments of time relative to the identified segments oftime for the identified segment of time when determining the parameterscore for each of the plurality of physiological sensors.
 55. The methodof claim 53, wherein the adjusting comprises: using factors frompreceding or successive segments of time relative to the identifiedsegments of time for the identified segment of time when determining theparameter score for each of the plurality of physiological sensors.